The latest advance in the field of AI which is significantly impacting the marketing discipline is generative AI, representing a group of AI models that generate new content, including text, images, and videos. Generative AI differs from previous models through the ability to create novel content and perform key innovative marketing activities. Generative AI could transform the marketing innovations, processes and customer responses by being the most advanced form of feeling AI. Generative AI is defined as deep neural networks, pre-trained on large amounts of data to create foundation model, following human instructions to produce new content. Generative AI consists of self-supervised models that can predict an omitted word, given its surrounding text or a content of a patched image based on the remaining image information. Generative AI output novel content is based on the user prompt that it receives, thus, the next word or image feature could differ at each iteration and produce several responses from the same prompt, making hard to detect content generated by generative AI. Recent studies imply that generative AI is able to generate innovative ideas, and has capabilities that encompass divergent and analogical thinking, and inductive reasoning. For instance, text to image AI models can significantly outperform human-made benchmark images in terms of quality, realism and aesthetics. Also, these AI models can outperform human freelancers on several marketing outcome metrics at a fraction of a cost. Further, managers are relying on Generative AI for decision-making due to reasoning and occasional causal inference capabilities. However, current Generative AI are unable to process physical data measurements to validate their available textual facts, which explain the limitation on text-based inputs lacking embodiment and sensory stimuli.
Despite the advancement in AI research and applications, some important areas of AI developments require future considerations. AI algorithms have good predictive accuracy in forecasting the sales of current products. However, data on new products is not readily available, thus marketers need to understand how best to combine AI-driven insights with human judgment for new products. The level and frequency of advertising may be less due to AI ability in predicting customer preferences. Marketers need to experiment with AI for better customer communications to increase engagement and repurchase. For example, how AI can examine customer information from social media posts and combine it with previous communication to develop persuasive messages that increase engagement is an area for experimentation. Also, businesses can start using AI to support salespeople in real-time by providing feedback on customers’ verbal and facial responses. This implies that organisations have to re-think their structures in the presence of AI. Companies need to manage the trade-off between AI focussing on customer expressed needs versus employees that may be relatively better able to manage issues like customer stewardship. Companies need to train their employees on the use of AI and the management of customer concerns related to data privacy ad ethics. Customer views on AI remain negative due to their sense that AI is unable to feel or identify their uniqueness, leading to less being empathetic. Therefore, positioning AI as learning artificial organism that collaborates with human inputs to provide better solution and answers may help mitigating customer negative views about AI. Also, customers might have an ideal preferences which may differ from their past behaviours, and AI could make it harder for them to find and move toward their preferred options because of its reflection of past behaviours. Multinational companies face the challenge of managing communications with international customers that embed different culture, behaviours and perceptions about AI applications. These companies need to examine how customers from different countries perceive AI applications and ethical usage. Data privacy is an important topic for future consideration in regard to AI tools. The balance between little protection which prevents customers from using AI and too much regulation that strangle innovation is required. Finally, companies need to develop realistic expectations about the impacts of AI on their business processes and outcomes in the short and long run. AI seems to be more effective if it is deployed in ways that augment rather than replace human. This approach allows companies and marketers to be ready for future AI developments and evolutionary benefits.
References:
Cillo, P., & Rubera, G. (2025). Generative AI in innovation and marketing processes: A roadmap of research opportunities. Journal of the Academy of Marketing Science, 53(3), 684-701.
Hartmann, J., Exner, Y., & Domdey, S. (2025). The power of generative marketing: Can generative AI create superhuman visual marketing content?. International Journal of Research in Marketing, 42(1), 13-31.
Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the academy of marketing science, 49(1), 30-50.
Huang, M. H., & Rust, R. T. (2022). A framework for collaborative artificial intelligence in marketing. Journal of Retailing, 98(2), 209-223.
